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POHow do I translate this 'sparse' Matlab bsxfun call to R?
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2012-09-28T20:47:26.230
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<pre><code>>> A = sparse([1,2,3,4,5]) A = (1,1) 1 (1,2) 2 (1,3) 3 (1,4) 4 (1,5) 5 >> B = sparse([1;2;3;4;5]) B = (1,1) 1 (2,1) 2 (3,1) 3 (4,1) 4 (5,1) 5 >> bsxfun(@times, A, B) ans = (1,1) 1 (2,1) 2 (3,1) 3 (4,1) 4 (5,1) 5 (1,2) 2 (2,2) 4 (3,2) 6 (4,2) 8 (5,2) 10 (1,3) 3 (2,3) 6 (3,3) 9 (4,3) 12 (5,3) 15 (1,4) 4 (2,4) 8 (3,4) 12 (4,4) 16 (5,4) 20 (1,5) 5 (2,5) 10 (3,5) 15 (4,5) 20 (5,5) 25 </code></pre> Which looks like this in non-sparse form: <pre><code>>> full(ans) ans = 1 2 3 4 5 2 4 6 8 10 3 6 9 12 15 4 8 12 16 20 5 10 15 20 25 >> </code></pre> EDIT: I would like to do a matrix multiplication of these sparse vectors, and return a sparse array: <pre><code>> class(NRowSums) [1] "dsparseVector" attr(,"package") [1] "Matrix" > class(NColSums) [1] "dsparseVector" attr(,"package") [1] "Matrix" > </code></pre> NRowSums * NColSums (I think; or if that returns a scalar, then flip them) w/o using a non-sparse variable to temporarily store data. EDIT2: I currently have this: <pre><code>NSums = tcrossprod(as(NRowSums, "sparseMatrix"), as(NColSums, "sparseMatrix")) </code></pre> This seems a bit awkward for what I'm trying to do, especially the type castings. It's also extremely inneficient, because it computes all elements where either a NRowSum or NColSum exist, and not only the intersection of these two. That is, there are about 100x more entries in this NSums than in the original sparse matrix.
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<r><matlab><vectorization><bsxfun>
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How do I translate this 'sparse' Matlab bsxfun call to R?
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